code: def __init__(
      self,
      d_data: int,
      d_feats: int,
      d_hidden: int,
      d_value: int,
      *window_size: int,
      tie_casts: bool = False,
  ) -> None:
      super(EncoderModule, self).__init__()
      self.d_data = d_data
      self.d_feats = d_feats
      self.d_hidden = d_hidden
      self.window_size = window_size
      self.tie_casts = tie_casts
      assert len(self.window_size) <= self.d_hidden
  
      d_shape = [d_hidden // len(window_size)] * len(window_size)
      d_shape[-1] += self.d_hidden % len(window_size)
      self._shape_weights = nn.ParameterList()
      self._shape_biases = nn.ParameterList()
      for d_s, wsize in zip(d_shape, window_size):
          self._shape_weights.append(
              nn.Parameter(Tensor(d_s, d_data + d_feats, wsize))
          )
          self._shape_biases.append(nn.Parameter(Tensor(d_s)))
  
      self._inter_weight = nn.Parameter(
          Tensor(d_value, d_data + d_feats)
      )
      self._inter_bias = nn.Parameter(Tensor(d_value))
      if self.tie_casts:
          self._extra_weight = self._inter_weight
          self._extra_bias = self._inter_bias
      else:
          self._extra_weight = nn.Parameter(
              Tensor(d_value, d_data + d_feats)
          )
          self._extra_bias = nn.Parameter(Tensor(d_value))
  
      self._reset_parameters()
  
imports: from typing import Optional, List, Tuple, Union
  import numpy
  import torch
  from torch import Tensor, BoolTensor, nn
  from torch.nn import init, functional
  from tslib.nn.transformer import PositionwiseFFN
  from .kernel import AttentionKernel
  from .disc import SimpleDiscriminator
label_desc: Use image node_id 1 to create a new EncoderModule object from inherited base classes: nn with example: obj = EncoderModule(d_data, d_feats, d_hidden, d_value)